Thanks for the reply Reuti, There are two machines: Node1 with 12 physical cores (dual 6 core Xeon) and Node2 with 4 physical cores (i5-2400).
Regarding scaling on the single 12 core node, not it is also not linear. In fact it is downright strange. I do not remember the numbers right now but 10 jobs are faster than 11 and 12 are the fastest with peak performance of approximately 66 Msu/s which is also far from triple the 4 core performance. This odd non-linear behaviour also happens at the lower job counts on that 12 core node. I understand the decrease in scaling with increase in core count on the single node as the memory bandwidth is an issue. On the 4 core machine the scaling is progressive, ie. every additional job brings an increase in performance. Single core delivers 8.1 Msu/s while 4 cores deliver 30.8 Msu/s. This is almost linear. Since my original email I have also installed Open-MX and recompiled OpenMPI to use it. This has resulted in approximately 10% better performance using the existing GbE hardware. On 29 January 2014 19:40, Reuti <re...@staff.uni-marburg.de> wrote: > Am 29.01.2014 um 03:00 schrieb Victor: > > > I am running a CFD simulation benchmark cavity3d available within > http://www.palabos.org/images/palabos_releases/palabos-v1.4r1.tgz > > > > It is a parallel friendly Lattice Botlzmann solver library. > > > > Palabos provides benchmark results for the cavity3d on several different > platforms and variables here: > http://wiki.palabos.org/plb_wiki:benchmark:cavity_n400 > > > > The problem that I have is that the benchmark performance on my cluster > does not scale even close to a linear scale. > > > > My cluster configuration: > > > > Node1: Dual Xeon 5560 48 Gb RAM > > Node2: i5-2400 24 Gb RAM > > > > Gigabit ethernet connection on eth0 > > > > OpenMPI 1.6.5 on Ubuntu 12.04.3 > > > > > > Hostfile: > > > > Node1 -slots=4 -max-slots=4 > > Node2 -slots=4 -max-slots=4 > > > > MPI command: mpirun --mca btl_tcp_if_include eth0 --hostfile > /home/mpiuser/.mpi_hostfile -np 8 ./cavity3d 400 > > > > Problem: > > > > cavity3d 400 > > > > When I run mpirun -np 4 on Node1 I get 35.7615 Mega site updates per > second > > When I run mpirun -np 4 on Node2 I get 30.7972 Mega site updates per > second > > When I run mpirun --mca btl_tcp_if_include eth0 --hostfile > /home/mpiuser/.mpi_hostfile -np 8 ./cavity3d 400 I get 47.3538 Mega site > updates per second > > > > I understand that there are latencies with GbE and that there is MPI > overhead, but this performance scaling still seems very poor. Are my > expectations of scaling naive, or is there actually something wrong and > fixable that will improve the scaling? Optimistically I would like each > node to add to the cluster performance, not slow it down. > > > > Things get even worse if I run asymmetric number of mpi jobs in each > node. For instance running -np 12 on Node1 > > Isn't this overloading the machine with only 8 real cores in total? > > > > is significantly faster than running -np 16 across Node1 and Node2, thus > adding Node2 actually slows down the performance. > > The i5-2400 has only 4 cores and no threads. > > It depends on the algorithm how much data has to be exchanged between the > processes, and this can indeed be worse when used across a network. > > Also: is the algorithm scaling linear when used on node1 only with 8 > cores? When it's "35.7615 " with 4 cores, what result do you get with 8 > cores on this machine. > > -- Reuti > _______________________________________________ > users mailing list > us...@open-mpi.org > http://www.open-mpi.org/mailman/listinfo.cgi/users >
